Example Datasets

Download, preview, and query example datasets for use in cartoframes examples. Try examples by running the notebooks in binder

In addition to the functions listed below, this examples module is authenticated against all public datasets in the https://cartoframes.carto.com account. This means that besides reading the datasets from CARTO, users can also create maps from these datasets.

For example, the following will produce an interactive map of poverty rates in census tracts in Brooklyn, New York (preview of static version below code).

from cartoframes.examples import examples

examples.map('brooklyn_poverty')
https://cartoframes.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid2_labels1_zoom0/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2F%7Bs%7D.basemaps.cartocdn.com%2Frastertiles%2Fvoyager_nolabels%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer+%7B++polygon-fill%3A+ramp%28%5Bpoverty_per_pop%5D%2C+cartocolor%28Mint%29%2C+quantiles%285%29%2C+%3E%29%3B+polygon-opacity%3A+0.9%3B+polygon-gamma%3A+0.5%3B+line-color%3A+%23FFF%3B+line-width%3A+0.5%3B+line-opacity%3A+0.25%3B+line-comp-op%3A+hard-light%3B%7D%23layer%5Bpoverty_per_pop+%3D+null%5D+%7B++polygon-fill%3A+%23ccc%3B%7D%22%2C+%22sql_0%22%3A+%22SELECT+%2A+FROM+brooklyn_poverty%22%7D&anti_cache=0.2903456538919632&bbox=-74.041916%2C40.569596%2C-73.833422%2C40.739158

To query datasets, use the Dataset class. The following example finds the poverty rate in the census tract a McDonald’s fast food joint is located (preview of static map below code).

from cartoframes.examples import examples
from cartoframes.data import Dataset

# query to get poverty rates where mcdonald's are located in brooklyn
query = '''
            SELECT m.the_geom, m.cartodb_id, m.the_geom_webmercator, c.poverty_per_pop
            FROM mcdonalds_nyc as m, brooklyn_poverty as c WHERE ST_Intersects(m.the_geom, c.the_geom)
        '''

credentials = examples.get_credentials()
ds = Dataset(query, credentials=credentials)

# download and show the data
ds.download()
ds.dataframe

# map
examples.map(ds)
https://cartoframes.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid2_labels0_zoom0/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2F%7Bs%7D.basemaps.cartocdn.com%2Frastertiles%2Fvoyager_labels_under%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer+%7B++marker-width%3A+ramp%28%5Bpoverty_per_pop%5D%2C+range%285%2C25%29%2C+quantiles%285%29%29%3B+marker-fill%3A+%235D69B1%3B+marker-fill-opacity%3A+0.9%3B+marker-allow-overlap%3A+true%3B+marker-line-width%3A+0.5%3B+marker-line-color%3A+%23FFF%3B+marker-line-opacity%3A+1%3B%7D%22%2C+%22sql_0%22%3A+%22%5CnSELECT+m.the_geom%2C+m.cartodb_id%2C+m.the_geom_webmercator%2C+c.poverty_per_pop%5CnFROM+mcdonalds_nyc+as+m%2C+brooklyn_poverty+as+c%5CnWHERE+ST_Intersects%28m.the_geom%2C+c.the_geom%29%5Cn%22%7D&anti_cache=0.040403611167980635&bbox=-74.0277516749999%2C40.57955036%2C-73.8603420299999%2C40.7303652850001

To write datasets to your account from the examples account, the following is a good method:

from cartoframes.auth import Credentials
from cartoframes.data import Dataset
from cartoframes.examples import read_taxi

USERNAME = 'your user name'
APIKEY = 'your API key'

credentials = Credentials(
    username=USERNAME,
    api_key=APIKEY
)

Dataset(read_taxi()).upload(
  table_name='taxi_data_examples_acct',
  lnglat=('pickup_latitude', 'pickup_longitude')
  credentials=credentials)

Data access functions

cartoframes.examples.read_brooklyn_poverty(limit=None, **kwargs)

Read the dataset brooklyn_poverty into a pandas DataFrame from the cartoframes example account at https://cartoframes.carto.com/tables/brooklyn_poverty/public This dataset contains poverty rates for census tracts in Brooklyn, New York

The data looks as follows (styled on poverty_per_pop):

https://cartoframes.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid2_labels1_zoom0/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2F%7Bs%7D.basemaps.cartocdn.com%2Frastertiles%2Fvoyager_nolabels%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer+%7B++polygon-fill%3A+ramp%28%5Bpoverty_per_pop%5D%2C+cartocolor%28Mint%29%2C+quantiles%285%29%2C+%3E%29%3B+polygon-opacity%3A+0.9%3B+polygon-gamma%3A+0.5%3B+line-color%3A+%23FFF%3B+line-width%3A+0.5%3B+line-opacity%3A+0.25%3B+line-comp-op%3A+hard-light%3B%7D%23layer%5Bpoverty_per_pop+%3D+null%5D+%7B++polygon-fill%3A+%23ccc%3B%7D%22%2C+%22sql_0%22%3A+%22SELECT+%2A+FROM+brooklyn_poverty%22%7D&anti_cache=0.10952614318949128&bbox=-74.041916%2C40.569596%2C-73.833422%2C40.739158
Parameters:
  • limit (int, optional) – Limit results to limit. Defaults to return all rows of the original dataset
  • **kwargs – Arguments accepted in Dataset.download
Returns:

Data in the table brooklyn_poverty on the cartoframes example account

Return type:

pandas.DataFrame

Example:

from cartoframes.examples import read_brooklyn_poverty
df = read_brooklyn_poverty()
cartoframes.examples.read_mcdonalds_nyc(limit=None, **kwargs)

Read the dataset mcdonalds_nyc into a pandas DataFrame from the cartoframes example account at https://cartoframes.carto.com/tables/mcdonalds_nyc/public This dataset contains the locations of McDonald’s Fast Food within New York City.

Visually the data looks as follows:

https://cartoframes.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid2_labels0_zoom0/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2F%7Bs%7D.basemaps.cartocdn.com%2Frastertiles%2Fvoyager_labels_under%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer+%7B++marker-width%3A+10%3B+marker-fill%3A+%235D69B1%3B+marker-fill-opacity%3A+0.9%3B+marker-allow-overlap%3A+true%3B+marker-line-width%3A+0.5%3B+marker-line-color%3A+%23FFF%3B+marker-line-opacity%3A+1%3B%7D%22%2C+%22sql_0%22%3A+%22SELECT+%2A+FROM+mcdonalds_nyc%22%7D&anti_cache=0.5738692383372218&bbox=-74.1691323509999%2C40.5594463460001%2C-73.7431178569999%2C40.892981078
Parameters:
  • limit (int, optional) – Limit results to limit. Defaults to return all rows of the original dataset
  • **kwargs – Arguments accepted in Dataset.download
Returns:

Data in the table mcdonalds_nyc on the cartoframes example account

Return type:

pandas.DataFrame

Example:

from cartoframes.examples import read_mcdonalds_nyc
df = read_mcdonalds_nyc()
cartoframes.examples.read_nat(limit=None, **kwargs)

Read nat dataset: US county homicides 1960-1990

This table is located at: https://cartoframes.carto.com/tables/nat/public

Visually, the data looks as follows (styled by the hr90 column):

https://cartoframes.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid2_labels1_zoom0/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2F%7Bs%7D.basemaps.cartocdn.com%2Frastertiles%2Fvoyager_nolabels%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer+%7B++polygon-fill%3A+ramp%28%5Bhr90%5D%2C+cartocolor%28Sunset%29%2C+quantiles%287%29%2C+%3E%29%3B+polygon-opacity%3A+0.9%3B+polygon-gamma%3A+0.5%3B+line-color%3A+%23FFF%3B+line-width%3A+0.5%3B+line-opacity%3A+0.25%3B+line-comp-op%3A+hard-light%3B%7D%23layer%5Bhr90+%3D+null%5D+%7B++polygon-fill%3A+%23ccc%3B%7D%22%2C+%22sql_0%22%3A+%22SELECT+%2A+FROM+nat%22%7D&anti_cache=0.9906959573885755&bbox=-124.731422424316%2C24.9559669494629%2C-66.9698486328125%2C49.3717346191406
Parameters:
  • limit (int, optional) – Limit results to limit. Defaults to return all rows of the original dataset
  • **kwargs – Arguments accepted in Dataset.download
Returns:

Data in the table nat on the cartoframes example account

Return type:

pandas.DataFrame

Example:

from cartoframes.examples import read_nat
df = read_nat()
cartoframes.examples.read_nyc_census_tracts(limit=None, **kwargs)

Read the dataset nyc_census_tracts into a pandas DataFrame from the cartoframes example account at https://cartoframes.carto.com/tables/nyc_census_tracts/public This dataset contains the US census boundaries for 2015 Tiger census tracts and the corresponding GEOID in the geom_refs column.

Visually the data looks as follows:

https://cartoframes.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid2_labels1_zoom0/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2F%7Bs%7D.basemaps.cartocdn.com%2Frastertiles%2Fvoyager_nolabels%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer+%7B++polygon-fill%3A+%235D69B1%3B+polygon-opacity%3A+0.9%3B+polygon-gamma%3A+0.5%3B+line-color%3A+%23FFF%3B+line-width%3A+0.5%3B+line-opacity%3A+0.25%3B+line-comp-op%3A+hard-light%3B%7D%22%2C+%22sql_0%22%3A+%22SELECT+%2A+FROM+nyc_census_tracts%22%7D&anti_cache=0.05019415422872964&bbox=-74.25909%2C40.477399%2C-73.70002%2C40.917577
Parameters:
  • limit (int, optional) – Limit results to limit. Defaults to return all rows of the original dataset
  • **kwargs – Arguments accepted in Dataset.download
Returns:

Data in the table nyc_census_tracts on the cartoframes example account

Return type:

pandas.DataFrame

Example:

from cartoframes.examples import read_nyc_census_tracts
df = read_nyc_census_tracts()
cartoframes.examples.read_taxi(limit=None, **kwargs)

Read the dataset taxi_50k into a pandas DataFrame from the cartoframes example account at https://cartoframes.carto.com/tables/taxi_50k/public. This table has a sample of 50,000 taxi trips taken in New York City. The dataset includes fare amount, tolls, payment type, and pick up and drop off locations.

Note

This dataset does not have geometries. The geometries have to be created by using the pickup or drop-off lng/lat pairs. These can be specified in Dataset.upload.

To create geometries with examples.query, write a query such as this:

from cartoframes.client import SQLClient

sql = SQLClient(examples.get_credentials())

sql.query('''
    SELECT
      CDB_LatLng(pickup_latitude, pickup_longitude) as the_geom,
      cartodb_id,
      fare_amount
    FROM
      taxi_50
''')

The data looks as follows (using the pickup location for the geometry and styling by fare_amount):

https://cartoframes.carto.com/api/v1/map/static/named/cartoframes_ver20170406_layers1_time0_baseid2_labels0_zoom1/800/400.png?config=%7B%22basemap_url%22%3A+%22https%3A%2F%2F%7Bs%7D.basemaps.cartocdn.com%2Frastertiles%2Fvoyager_labels_under%2F%7Bz%7D%2F%7Bx%7D%2F%7By%7D.png%22%2C+%22cartocss_0%22%3A+%22%23layer+%7B++marker-width%3A+10%3B+marker-fill%3A+ramp%28%5Bfare_amount%5D%2C+cartocolor%28Mint%29%2C+quantiles%285%29%2C+%3E%29%3B+marker-fill-opacity%3A+0.9%3B+marker-allow-overlap%3A+true%3B+marker-line-width%3A+0.5%3B+marker-line-color%3A+%23FFF%3B+marker-line-opacity%3A+1%3B%7D%23layer%5Bfare_amount+%3D+null%5D+%7B++marker-fill%3A+%23ccc%3B%7D%22%2C+%22sql_0%22%3A+%22%5Cn++++WITH+cte+as+%28%5Cn++++SELECT+CDB_LatLng%28pickup_latitude%2C+pickup_longitude%29+as+the_geom%2C+cartodb_id%2C+fare_amount%5Cn++++FROM+taxi_50k%5Cn++++%29%5Cn++++SELECT+%2A%2C+ST_Transform%28the_geom%2C+3857%29+as+the_geom_webmercator%5Cn++++FROM+cte%5Cn%22%7D&anti_cache=0.24523273064119488&zoom=9&lat=40.7743&lon=-73.916
Parameters:
  • limit (int, optional) – Limit results to limit. Defaults to return all rows of the original dataset
  • **kwargs – Arguments accepted in Dataset.download
Returns:

Data in the table taxi_50k on the cartoframes example account

Return type:

pandas.DataFrame

Example:

from cartoframes.examples import read_taxi
df = read_taxi()

Examples class

class cartoframes.examples.Examples

This special class provides read access to all the datasets in the cartoframes CARTO account.

The recommended way to use this class is to import the examples from the cartoframes.examples module:

from cartoframes.examples import examples
df = examples.read_taxi()

The following tables are available:

  • brooklyn_poverty - basic poverty information for Brooklyn, New York
  • mcdonalds_nyc - McDonald’s locations in New York City
  • nat - historical USA-wide homicide rates at the county level
  • nyc_census_tracts - Census tract boundaries for New York City
  • taxi_50k - Taxi trip data, including pickup/drop-off locations. This table does not have an explicit geometry, so one must be created from the pickup_latitude/pickup_longitude columns, the dropoff_latitude/dropoff_longitude columns, or through some other process. When writing this table to your account, make sure to specify the lnglat flag in Dataset.upload

This class includes a convenience method for each of the tables listed above. See the full list below.

read_brooklyn_poverty(limit=None, **kwargs)

Poverty information for Brooklyn, New York, USA. See the function read_brooklyn_poverty for more information.

Example:

from cartoframes.examples import examples
df = examples.read_brooklyn_poverty()
read_mcdonalds_nyc(limit=None, **kwargs)

McDonald’s locations for New York City, USA. See the function read_mcdonalds_nyc for more information

Example:

from cartoframes.examples import examples
df = examples.read_mcdonalds_nyc()
read_nat(limit=None, **kwargs)

Historical homicide rates for the United States at the county level. See the function read_nat for more information

Example:

from cartoframes.examples import examples
df = examples.read_nat()
read_nyc_census_tracts(limit=None, **kwargs)

Census tracts for New York City, USA. See the function read_nyc_census_tracts for more information

Example:

from cartoframes.examples import examples
df = examples.read_nyc_census_tracts()
read_taxi(limit=None, **kwargs)

Taxi pickup and drop-off logs for New York City, USA. See the function read_taxi for more information

Example:

from cartoframes.examples import examples
df = examples.read_taxi()